Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance and have been applied to the task of the classification of real and bogus transients. Different from most existing approaches which necessitate massive yet expensive annotated data, We aim to leverage training samples with only 1000 labels available to discover real sources that vary in brightness over time in the early stage of the WFST 6-year survey. Methods. We present a novel deep-learning method that combines active learning and semi-supervised learning to construct a competitive real/bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model's performance and achieve superior results compared to using only the limited labeled data.
Investigating the Effects of Point Source Injection Strategies on KMTNet Real/Bogus Classification
Pohang University of Science and Technology (POSTECH)（浦项科技大学）；Institute for Astronomy, University of Hawaii（夏威夷大学天体物理研究所）；Seoul National University（首尔国立大学）
M-EPDet: Real-Time Real-Bogus Classification and Transient Candidate Judgement for the EP-WXT Pipeline via Multi-Modal Data
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；University of Chinese Academy of Sciences（中国科学院大学）；National Astronomical Data Center（国家天文数据中心）
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Università degli Studi di Milano, Department of Computer Science（米兰大学计算机科学系）；Aix Marseille Univ, CNRS/IN2P3, CPPM（阿ix马赛大学，CNRS/IN2P3，CPPM）；Université Côte d’Azur, INRIA, CNRS, Laboratoire J.A.Dieudonné, Maasai team（滨海大学，INRIA，CNRS，J.A.Dieudonné实验室，Maasai团队）；Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange（滨海大学，滨海天文台，CNRS，Lagrange实验室）；Université de Liège, STAR Institute（列日大学，STAR研究所）
A GPU-Accelerated Transient Detection Pipeline for DECam Time-Domain Surveys
Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）；Texas A. & M. University（德克萨斯农工大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning
The University of Manchester（曼彻斯特大学）；Radboud University（拉德堡德大学）；University of Cape Town（开普敦大学）；South African Astronomical Observatory（南非天文台）；Edge Hill University（边山大学）；Dataspex B.V.（Dataspex公司）；Leiden University（莱顿大学）；Northwestern University（西北大学）
Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts
Universidad de Concepción（康塞普西翁大学）；Millennium Institute of Astrophysics（千年天体物理研究所）；University of Chile（智利大学）；Universidad de Chile（智利大学）；Fintual Administradora General de Fondos S.A.（Fintual 基金管理公司）
Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map
Monash University（莫纳什大学）；OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery（OzGrav：ARC引力波发现卓越中心）；University of Warwick（华威大学）；Institute for Globally Distributed Open Research and Education (IGDORE)（全球分布式开放研究与教育研究所（IGDORE））；University of Sheffield（谢菲尔德大学）
Department of Computer Science and Engineering, Indian Institute of Technology, Gandhinagar, India（印度理工学院计算机科学与工程系）；Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院物理、数学与天文学系）；Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA 91125, USA（数据驱动发现中心）；IPAC, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA（IPAC, 加州理工学院）；Caltech Optical Observatories, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院光学观测站）
Transformer-Based Neural Network for Transient Detection without Image Subtraction
Department of Physics and Astronomy, University of Pennsylvania（物理与天文学系，宾夕法尼亚大学）；Department of Physics and Astronomy, University of Delaware（物理与天文学系，德克萨斯大学）；University of Delaware, Joseph R. Biden, Jr. School of Public Policy and Administration（德克萨斯大学乔伊斯·拜登初级公共政策与行政学校）；University of Delaware, Data Science Institute（德克萨斯大学数据科学研究所）
Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors
Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所微重力实验室引力波实验中心）；Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS)（中国科学院大学太极引力波宇宙实验室（北京/杭州））
Lomonosov Moscow State University（莫斯科国立大学）；National Research University Higher School of Economics（俄罗斯高等经济大学）；Ural Federal University（乌拉尔联邦大学）；Carnegie Mellon University（卡内基梅隆大学）
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks
Universidad de los Andes（洛斯安第斯大学）；Center for Research and Formation in Artificial Intelligence（人工智能研究与培训中心）；Systems and Computing Engineering Department（系统与计算工程系）；Departamento de Física（物理系）
Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours
活动星系核（AGN）的经典分类方案最近受到了所谓的变态（变貌）AGN（CSAGNs）的发现的挑战。这一现象背后的物理机制仍然是一个公开辩论的问题，样本太小，而且是偶然发现的，无法提供可靠的答案。为了解决这个问题，我们需要设计能够在改变状态的行为中检测AGN的方法。在这里，我们提出了一个异常检测（AD）技术，旨在识别活动星系核的光变曲线与异常行为在大量的数据集。这项技术的主要目的是在转变的不同阶段识别CSAGN，但它也可以用于更一般的目的，例如清理AGN可变性分析的大规模数据集。我们使用的光变曲线来自Zwicky Transient Facility数据发布5（ZTF DR 5），包含230，451个不同类别的AGN样本。ZTF DR 5光变曲线采用变分递归自动编码器（VRAE）架构建模，这使我们能够从VRAE潜在空间中获得一组属性，这些属性描述了我们样本的一般行为。然后，这些属性被用作隔离森林（IF）算法的特征，该算法是针对“一类”问题的异常检测器。我们使用VRAE重建误差和IF异常评分来选择8，809个异常的样本。这些异常主要是由虚假的候选人，但我们能够确定75个有希望的CSAGN候选人。
Unsupervised Deep Learning Method for Clustering KAGRA O3GK Transient Noise Data
Ewha Womans University（梨花女子大学）；National Institute for Mathematical Sciences（韩国数学科学研究所）；Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）
Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS
University of Pittsburgh（匹兹堡大学）；Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), University of Pittsburgh（匹兹堡大学匹兹堡粒子物理、天体物理学和宇宙学中心）；University of Toronto（多伦多大学）；Canadian Institute for Theoretical Astrophysics, University of Toronto（多伦多大学加拿大理论天体物理研究所）；Dunlap Institute for Astronomy & Astrophysics, University of Toronto（多伦多大学邓普勒天文与天体物理研究所）
Revealing Hidden Repeaters in the CHIME/FRB Catalog: Semi-Supervised Insights into the Fast Radio Burst Population
Suranaree University of Technology（素拉那瑞技术大学）；Srinakharinwirot University（斯里纳卡林威洛大学）；National Astronomical Research Institute of Thailand (Public Organization)（泰国国家天文研究所）
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
Department of Computer Science University of California Irvine（加利福尼亚大学尔湾分校计算机科学系）；Université Clermont Auvergne CNRS/IN2P3, LPC Clermont Ferrand（克莱蒙费朗大学（Université Clermont Auvergne）、法国国家科学研究中心/法国国家核物理与粒子物理研究所（CNRS/IN2P3）、克莱蒙费朗物理实验室（LPC Clermont Ferrand））；CENTRA-Centro de Astrofísica e Gravitação Universidade de Lisboa（里斯本大学（Universidade de Lisboa）中心天体物理与引力中心（CENTRA-Centro de Astrofísica e Gravitação））；Shanghai Astronomical Observatory Chinese Academy of Sciences（中国科学院上海天文台）；Berkeley Center for Cosmological Physics University of California Berkeley（加利福尼亚大学伯克利分校宇宙物理伯克利中心）；Department of Computer Science University of Houston（休斯顿大学计算机科学系）；Department of Physics and Astronomy The Johns Hopkins University（约翰斯·霍普金斯大学物理与天文系）；Department of Informatics University of California Irvine（加利福尼亚大学尔湾分校信息学系）；NSF’s NOIRLab（美国国家科学基金会国家光学红外天文学研究实验室（NOIRLab））；Gemini Observatory（双子座天文台）
Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
Universidade de Aveiro（阿威罗大学）；Centro de Física das Universidades do Minho e do Porto (CF-UM-UP)（米尼奥大学和波尔图大学物理中心（CF-UM-UP））；Universidade do Minho（米尼奥大学）；Instituto Galego de Física de Altas Enerxías（加利西亚高能物理研究所）；Universidade de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）；Department of Physics, The Chinese University of Hong Kong（香港中文大学物理系）；Universitat de València（瓦伦西亚大学）
Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
Tsung-Dao Lee Institute, Shanghai Jiao Tong University（上海交通大学李政道研究所）；University of Warwick（华威大学）；School of Physics and Astronomy, Shanghai Jiao Tong University（上海交通大学物理与天文学院）
Multiband neural network classification of ZTF light curves as LSST proxies
Konkoly Observatory, HUN-REN Research Centre for Astronomy and Earth Sciences（孔科利天文台，匈牙利研究网络天文学与地球科学研究院）；HUN-REN CSFK, MTA Centre of Excellence（匈牙利研究网络CSFK，马塔卓越中心）；Department of Astrophysical Sciences, Princeton University（普林斯顿大学天体物理科学系）；ELTE Eötvös Loránd University, Institute of Physics and Astronomy（罗兰大学，物理与天文学研究所）；MTA–HUN-REN CSFK Lendület "Momentum" Stellar Pulsation Research Group（马塔-匈牙利研究网络CSFK Lendület "Momentum" 恒星脉动研究组）
